OpenRaiser/Intern-Atlas huggingface dataset:
Dissecting the Graphcore IPU Architecture via Microbenchmarking
Venue: arXiv cs.DC 2019
Year: 2019

This report focuses on the architecture and performance of the Intelligence Processing Unit (IPU), a novel, massively parallel platform recently introduced by Graphcore and aimed at Artificial Intelligence/Machine Learning (AI/ML) workloads. We dissect the IPU's performance behavior using microbenchmarks that we crafted for the purpose. We study the IPU's memory organization and performance. We study the latency and bandwidth that the on-chip and off-chip interconnects offer, both in point-to-point transfers and in a spectrum of collective operations, under diverse loads. We evaluate the IPU's compute power over matrix multiplication, convolution, and AI/ML primitives. We discuss actual performance in comparison with its theoretical limits. Our findings reveal how the IPU's architectural design affects its performance. Moreover, they offer simple mental models to predict an application's performance on the IPU, on the basis of the computation and communication steps it involves. This report is the natural extension to a novel architecture of a continuing effort of ours that focuses on the microbenchmark-based discovery of massively parallel architectures.
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Venue: arXiv cs.DB 2019
Year: 2019

Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets requires blocking, a step that reduces the number of record pairs to be matched. However, most of the traditional blocking methods are learning-free and key-based, and their successes are largely built on laborious human effort in cleaning data and designing blocking keys. In this paper, we propose AutoBlock, a novel hands-off blocking framework for entity matching, based on similarity-preserving representation learning and nearest neighbor search. Our contributions include: (a) Automation: AutoBlock frees users from laborious data cleaning and blocking key tuning. (b) Scalability: AutoBlock has a sub-quadratic total time complexity and can be easily deployed for millions of records. (c) Effectiveness: AutoBlock outperforms a wide range of competitive baselines on multiple large-scale, real-world datasets, especially when datasets are dirty and/or unstructured.
Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment
Venue: arXiv cs.CV 2019
Year: 2019

Recognition of human actions and associated interactions with objects and the environment is an important problem in computer vision due to its potential applications in a variety of domains. The most versatile methods can generalize to various environments and deal with cluttered backgrounds, occlusions, and viewpoint variations. Among them, methods based on graph convolutional networks that extract features from the skeleton have demonstrated promising performance. In this paper, we propose a novel Spatio-Temporal Pyramid Graph Convolutional Network (ST-PGN) for online action recognition for ergonomic risk assessment that enables the use of features from all levels of the skeleton feature hierarchy. The proposed algorithm outperforms state-of-art action recognition algorithms tested on two public benchmark datasets typically used for postural assessment (TUM and UW-IOM). We also introduce a pipeline to enhance postural assessment methods with online action recognition techniques. Finally, the proposed algorithm is integrated with a traditional ergonomic risk index (REBA) to demonstrate the potential value for assessment of musculoskeletal disorders in occupational safety.
PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English
Venue: arXiv cs.CL 2019
Year: 2019

Over 800 languages are spoken across West Africa. Despite the obvious diversity among people who speak these languages, one language significantly unifies them all - West African Pidgin English. There are at least 80 million speakers of West African Pidgin English. However, there is no known natural language processing (NLP) work on this language. In this work, we perform the first NLP work on the most popular variant of the language, providing three major contributions. First, the provision of a Pidgin corpus of over 56000 sentences, which is the largest we know of. Secondly, the training of the first ever cross-lingual embedding between Pidgin and English. This aligned embedding will be helpful in the performance of various downstream tasks between English and Pidgin. Thirdly, the training of an Unsupervised Neural Machine Translation model between Pidgin and English which achieves BLEU scores of 7.93 from Pidgin to English, and 5.18 from English to Pidgin. In all, this work greatly reduces the barrier of entry for future NLP works on West African Pidgin English.
Programming Scalable Cloud Services with AEON
Venue: arXiv cs.DC 2019
Year: 2019

Designing low-latency cloud-based applications that are adaptable to unpredictable workloads and efficiently utilize modern cloud computing platforms is hard. The actor model is a popular paradigm that can be used to develop distributed applications: actors encapsulate state and communicate with each other by sending events. Consistency is guaranteed if each event only accesses a single actor, thus eliminating potential data races and deadlocks. However it is nontrivial to provide consistency for concurrent events spanning across multiple actors. This paper addresses this problem by introducing AEON: a framework that provides the following properties: (i) Programmability: programmers only need to reason about sequential semantics when reasoning about concurrency resulting from multi-actor events; (ii) Scalability: AEON runtime protocol guarantees serializable and starvation-free execution of multi-actor events, while maximizing parallel execution; (iii) Elasticity: AEON supports fine-grained elasticity enabling the programmer to transparently migrate individual actors without violating the consistency or entailing significant performance overheads. Our empirical results show that it is possible to combine the best of all the above three worlds without compromising on the application performance.
VoronoiNet: General Functional Approximators with Local Support
Venue: arXiv cs.CV 2019
Year: 2019

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it -- via a novel deep architecture -- into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.
Detection of False Positive and False Negative Samples in Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions.
SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery
Venue: arXiv cs.CV 2019
Year: 2019

Renewable energy such as solar power is critical to fight the ever more serious climate change. China is the world leading installer of solar panel and numerous solar power plants were built. In this paper, we proposed a deep learning framework named SolarNet which is designed to perform semantic segmentation on large scale satellite imagery data to detect solar farms. SolarNet has successfully mapped 439 solar farms in China, covering near 2000 square kilometers, equivalent to the size of whole Shenzhen city or two and a half of New York city. To the best of our knowledge, it is the first time that we used deep learning to reveal the locations and sizes of solar farms in China, which could provide insights for solar power companies, market analysts and the government.
High-Freedom Inverse Design with Deep Neural Network for Metasurface Filter in the Visible
Venue: arXiv cs.OH 2019
Year: 2019

In order to obtain a metasurface structure capable of filtering the light of a specific wavelength in the visible band, traditional method usually traverses the space consisting of possible designs, searching for a potentially satisfying device by performing iterative calculations to solve Maxwell's equations. In this paper, we propose a neural network that can complete an inverse design process to solve the problem. Compared with the traditional method, our method is much faster while competent of generating better devices with the desired spectrum. One of the most significant advantages is that it can handle a real spectrum as well as an artificial one. Besides, our method encompasses a high degree of freedom to generate devices, ensuring their generated spectra resemble desired ones and meeting the accuracy requirements without losing practicability in the manufacturing process.
A study on Modern Messaging Systems- Kafka, RabbitMQ and NATS Streaming
Venue: arXiv cs.DC 2019
Year: 2019

Distributed messaging systems form the core of big data streaming, cloud native applications and microservice architecture. With real-time critical applications there is a growing need for well-built messaging platform that is scalable, fault tolerant and has low latency. There are multiple modern messaging systems that have come up in the recent past, all with their own pros and cons. This has become problematic for the industry to decide which messaging system is the most suitable for a specific application. An in-depth study is required to decide which features of a messaging system meet the needs of the application. This survey paper outlines the modern messaging technologies and delves deep on three popular publisher/subscriber systems- Apache Kafka, RabbitMQ and NATS Streaming. The paper provides information about messaging systems, the use cases, similarities and differences of features to facilitate users to make an informed decision and also pave way for future research and development.
Hand and Body Blockage Measurements with Form-Factor User Equipment at 28 GHz
Venue: arXiv cs.IT 2019
Year: 2019

Blockage by the human hand/body is an important impairment in realizing practical millimeter wave wireless systems. Prior works on blockage modeling are either based on theoretical studies of double knife edge diffraction or its modifications, high-frequency simulations of electromagnetic effects, or measurements with experimental millimeter wave prototypes. While such studies are useful, they do not capture the form-factor constraints of user equipments (UEs). In this work, we study the impact of hand/body blockage with a UE at $28$ GHz built on Qualcomm's millimeter wave modem, antenna modules and beamforming solutions. We report five exhaustive and controlled studies with different types of hand holdings/grips, antenna types, and with directional/narrow beams. For both hard as well as loose hand grips, we report considerably lower blockage loss estimates than prior works. Critical in estimating the loss is the definition of a "region of interest" (RoI) around the UE where the impact of the hand/body is seen. Towards this goal, we define a RoI that includes the spatial area where significant energy is seen in either the no blockage or blockage modes. Our studies show that significant spatial area coverage improvement can be seen with loose hand grip due to hand reflections.
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem. However, in this paper we show that the capacity of the architecture has not been fully explored due to the inadequate utilization of the supervision information. Such insufficient utilization is caused by the supervision signal degradation in back propagation. Thus inspired, we propose a dually supervised method, named dually supervised FPN (DSFPN), to enhance the supervision signal when training the feature pyramid network (FPN). In particular, DSFPN is constructed by attaching extra prediction (i.e., detection or segmentation) heads to the bottom-up subnet of FPN. Hence, the features can be optimized by the additional heads before being forwarded to subsequent networks. Further, the auxiliary heads can serve as a regularization term to facilitate the model training. In addition, to strengthen the capability of the detection heads in DSFPN for handling two inhomogeneous tasks, i.e., classification and regression, the originally shared hidden feature space is separated by decoupling classification and regression subnets. To demonstrate the generalizability, effectiveness, and efficiency of the proposed method, DSFPN is integrated into four representative detectors (Faster RCNN, Mask RCNN, Cascade RCNN, and Cascade Mask RCNN) and assessed on the MS COCO dataset. Promising precision improvement, state-of-the-art performance, and negligible additional computational cost are demonstrated through extensive experiments. Code will be provided.
Parallel algorithm for numerical solution of heat equation in complex cylindrical domain
Venue: arXiv cs.CE 2019
Year: 2019

In this article we present a parallel algorithm for simulation of the heat conduction process inside the so-called pulse cryogenic cell. This simulation is important for designing the device for portion injection of working gases into ionization chamber of ion source. The simulation is based on the numerical solving of the quasilinear heat equation with periodic source in a multilayered cylindrical domain. For numerical solution the Alternating Direction Implicit (ADI) method is used. Due to the non-linearity of the heat equation the simple-iteration method has been applied. In order to ensure convergence of the iteration process, the adaptive time-step has been implemented. The parallelization of the calculation has been realized with shared memory application programming interface OpenMP and the performance of the parallel algorithm is in agreement with the case studies in literature.
Women in ISIS Propaganda: A Natural Language Processing Analysis of Topics and Emotions in a Comparison with Mainstream Religious Group
Venue: arXiv cs.CL 2019
Year: 2019

Online propaganda is central to the recruitment strategies of extremist groups and in recent years these efforts have increasingly extended to women. To investigate ISIS' approach to targeting women in their online propaganda and uncover implications for counterterrorism, we rely on text mining and natural language processing (NLP). Specifically, we extract articles published in Dabiq and Rumiyah (ISIS's online English language publications) to identify prominent topics. To identify similarities or differences between these texts and those produced by non-violent religious groups, we extend the analysis to articles from a Catholic forum dedicated to women. We also perform an emotional analysis of both of these resources to better understand the emotional components of propaganda. We rely on Depechemood (a lexical-base emotion analysis method) to detect emotions most likely to be evoked in readers of these materials. The findings indicate that the emotional appeal of ISIS and Catholic materials are similar
Resource and Placement Optimization for Multiple UAVs using Backhaul Tethered Balloons
Venue: arXiv cs.NI 2019
Year: 2019

This paper studies the improvement of the achievable end-to-end data rate of ground users assisted with unmanned aerial vehicles (UAVs) and tethered balloons (TBs). The goal is to maximize the end-to-end throughput of a network suffering from the absence of terrestrial infrastructure. First, we solve an integer linear programming problem to optimize the associations. Then, we solve the UAVs transmit powers optimally by converting the problem into a convex one. Subsequently, an efficient algorithm is proposed to optimize the UAVs placement. Finally, our mathematical formalism is illustrated with some selected numerical results that show the advantages provided by our proposed scheme.
Patch Aggregator for Scene Text Script Identification
Venue: arXiv cs.CV 2019
Year: 2019

Script identification in the wild is of great importance in a multi-lingual robust-reading system. The scripts deriving from the same language family share a large set of characters, which makes script identification a fine-grained classification problem. Most existing methods make efforts to learn a single representation that combines the local features by making a weighted average or other clustering methods, which may reduce the discriminatory power of some important parts in each script for the interference of redundant features. In this paper, we present a novel module named Patch Aggregator (PA), which learns a more discriminative representation for script identification by taking into account the prediction scores of local patches. Specifically, we design a CNN-based method consisting of a standard CNN classifier and a PA module. Experiments demonstrate that the proposed PA module brings significant performance improvements over the baseline CNN model, achieving the state-of-the-art results on three benchmark datasets for script identification: SIW-13, CVSI 2015 and RRC-MLT 2017.
Lightweight Container-based User Environment
Venue: arXiv cs.DC 2019
Year: 2019

Modern operating systems all support multi-users that users could share a computer simultaneously and not affect each other. However, there are some limitations. For example, privacy problem exists that users are visible to each other in terms of running processes and files. Moreover, users have little freedom to customize the system environment. Last, it is a burden for system administrator to safely manage and update system environment while satisfying multiple users. Facing the above problems, this paper proposes CUE, a Lightweight Container-based User Environment. CUE proposes a new notion that stands in between application container and operating system container:user container. CUE is able to give users more flexibility to customize their environment, achieve privacy isolation, and make system update easier and safer. Its goal is to optimize and enhance the multi-user notion of current operating system and being lightweight. Moreover, it is able to facilitate application deployment in high performance clusters. It is currently deployed in NUDT's Tianhe E prototype supercomputer. Experiment results show that it introduces negligible overhead.
Transformer Based Reinforcement Learning For Games
Venue: arXiv cs.LG 2019
Year: 2019

Recent times have witnessed sharp improvements in reinforcement learning tasks using deep reinforcement learning techniques like Deep Q Networks, Policy Gradients, Actor Critic methods which are based on deep learning based models and back-propagation of gradients to train such models. An active area of research in reinforcement learning is about training agents to play complex video games, which so far has been something accomplished only by human intelligence. Some state of the art performances in video game playing using deep reinforcement learning are obtained by processing the sequence of frames from video games, passing them through a convolutional network to obtain features and then using recurrent neural networks to figure out the action leading to optimal rewards. The recurrent neural network will learn to extract the meaningful signal out of the sequence of such features. In this work, we propose a method utilizing a transformer network which have recently replaced RNNs in Natural Language Processing (NLP), and perform experiments to compare with existing methods.
High performance computing and energy efficiency: focus on OpenFOAM
Venue: arXiv cs.CY 2019
Year: 2019

High performance calculation is increasingly used within society. Previously reserved for an elite, based on large computing and storage infrastructures, it is now a core module for many companies. Indeed, high performance calculation makes it possible to design and optimize many elements for a limited cost ,compared to the production of prototypes or tests in situ. It is also widely used in big data and artificial intelligence.It seems essential to ask about theenvironmental impact of these digital practices. A number of actions have already been initiated in this community: GREEN500; European CoC eco-responsibility label for Data centres... but these actions generally look at specific or even idealised situations and/or software.The software qualification process in the field of high performance calculation consists in looking at the scalability of the software. The originality of this study is to focus on energy scalability (calculation return time depending on the power consumed), by considering several architectures (three TOP500 machines and a laboratory cluster).The energy cost of an example calculation could be estimated, which shows that the most efficient machine in terms of calculation time is not necessarily the most energy efficient, and depending on the number of cores/processes chosen, it is not always the same architecture that is the most energy-efficient. It was therefore possible to show that the longer the user is prepared to wait, the less energy is used by the calculation.
Why I killed my copper -- Highlights about the FTTO in the ESR
Venue: arXiv cs.NI 2019
Year: 2019

FTTO means Fiber To The Office, in reference to FTTH (Fibre To The Home), deployed in France for individuals. The principle of FTTO is to cable a building totally in fibre optic, to remove as much copper cabling as possible and install microswitches in each office (duct or adjacent), as near the machines as possible. Users are still connected with standard RJ45 copper wiring. Through questions and answers, we will highlight the reasons why FFTO is a controlled and future-oriented technology.Over the last six years, several building projects within the perimeter of Higher Education and Research have chosen this technology and have seen or will see the light of day. Depending on the project, different topologies and technologies are possible. What is the feedback after these years? Is the result as expected? How is the solution experienced on a day-to-day basis? What security, how is a large switch assembly configured and maintained, what high availability is possible? How is Wi-Fi, IP telephony and all PoE devices integrated? Does FTTO contribute to eco-consumption? How can a FTTO call for tender be set up for a project, what are the essential elements to be included and what are the errors to be avoided at all costs? In the future, what is the life expectancy for its infrastructure and what speeds can be envisaged? The RESINFO FTTO Group is working to provide clear answers to all these questions and to share its experience with the community.